honda.co.jp Domain Breach Exposure Report
Risk Score
Massive event volume or critical assets compromised.
AI Findings Summary
The telemetry indicates a significant exposure impacting 5,106 clients and 104 employees, with a high volume of historical data breaches (4,104) and active infostealer logs (1,106) observed over the period of July 2025 to June 2026. The primary threat vectors appear to be infostealers like LummaC2, Redline, and Rhadamanthys, targeting services such as Honda's login portals and business platforms. The majority of the exposure originates from "Combolist sources," suggesting credential stuffing or account takeover attempts, with a notable concentration in Japan. The risk score of 80 warrants immediate attention, focusing on credential hygiene, multi-factor authentication enforcement, and monitoring for unauthorized access to client and employee accounts, particularly those associated with the identified targeted services.
Total Events
Employee Affected Events
Client Affected Events
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12-Month Events Timeline
Event volume by breach date, employee VS client
Infostealers VS Data Breaches
Live stealer logs VS data breaches
104 compromised employee accounts pose an infrastructure risk, while 5,106 leaked client credentials create regulatory liability.
Antivirus Distribution
Security Tools on Infected Endpoints
Malware Families Distribution
Distribution of Active Stealer Strains
Top Login URLs
Top exposed services found in the event results
Infostealer By Geography
Shows the distribution of Infostealer-related credential exposure events across different geographic regions. The location is determined by analyzing the metadata of the infected machines associated with each event.
Country Breakdown
Services Classification Distribution
Blast radius - closer to core = more critical infrastructure, size = credential volume
Operating System Distribution
Distribution of compromised endpoint builds
Leak Repository Classification
Where the exposed records currently reside
Disclaimer: This report includes AI-generated content. AI can make mistakes, so verify important findings independently before taking action.